Image 3_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 5_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 2_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 1_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Table 1_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.docx by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 4_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

Image 6_Toward precision oncology in LUAD: a prognostic model using single-cell sequencing and WGCNA based on a disulfidptosis relative gene signature.tif by Panpan Li (484033)

“…Gene expression in single cell RNA sequencing (scRNA-seq) data was assessed using the AUcell algorithm. In the TCGA [LUAD] dataset, disulfidptosis-related enrichment scores were calculated using ssGSEA, and core gene sets were identified through the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. …”

CIAHS-Data.xls by Yingchang Li (22195585)

“…For this purpose, we employed the Natural Breaks classification method to reclassify factor values. This method identifies inherent natural grouping points within the data through the Jenks optimization algorithm, maximizing between-class differences while minimizing within-class differences37. …”

Dataset for Partial Parallelism Plot Analysis in Neurodegeneration Biomarker Assays (2010–2024) by Axel Petzold (7076261)

“…<br></p><p dir="ltr">Each dataset entry is annotated with:</p><ul><li>Sample type (serum, plasma, cerebrospinal fluid)</li><li>Assay platform and dilution steps</li><li>Classification of outcome (partial parallelism achieved or not)</li></ul><p dir="ltr"><b>Use cases:</b><br>This dataset is designed to help researchers, assay developers, and meta-analysts to:</p><ul><li>Reproduce figures and analyses from the published review</li><li>Benchmark or validate new assay performance pipelines</li><li>Train algorithms for automated detection of dilutional non-parallelism</li></ul><p dir="ltr"><b>Files included:</b></p><ul><li><code>.csv</code> files containing dilution–response data</li><li>Metadata spreadsheets with assay and sample annotations</li></ul><p></p>…”

<b>Research on Semantic Segmentation of PCB Point Clouds Based on Adaptive Dynamic Graph Convolution</b> by zedong huang (22221292)

“…In recent years, graph convolution networks (GCNs) have garnered increasing attention, particularly in the realm of Non-Euclidean data processing. Against this backdrop, our research proposes a point cloud segmentation method for electronic components based on Adaptive Dynamic Graph Convolution. …”

Early Parkinson’s disease identification via hybrid feature selection from multi-feature subsets and optimized CatBoost with SMOTE by Subhashree Mohapatra (17387852)

“…The analysis was conducted on a PD dataset derived from speech recording signals. To address the data imbalance, the synthetic minority oversampling technique (SMOTE) is applied as a pre-processing step to improve the robustness and reliability of the model. …”

Additional file 1 of The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László (21420583)

“…There is provided further information about data collection and processing, machine learning algorithms, and other program codes, and more details about the findings…”

Cell type classification in the 9-dpg leaves. by Frances K. Clark (5617169)

“…Associated with <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s010" target="_blank">S10 Fig</a>. The code and data associated with this figure can be found at Open Science Framework (osf.io), <a href="https://doi.org/10.17605/OSF.IO/RFCWS" target="_blank">https://doi.org/10.17605/OSF.IO/RFCWS</a>.…”

Cell type classification in the leaf and sepal. by Frances K. Clark (5617169)

“…See also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s009" target="_blank">S9</a> and <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003469#pbio.3003469.s011" target="_blank">S11 Figs</a>. The code and data associated with this figure can be found at Open Science Framework (osf.io), <a href="https://doi.org/10.17605/OSF.IO/RFCWS" target="_blank">https://doi.org/10.17605/OSF.IO/RFCWS</a>.…”

Paeameter ranges and optimal values. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Datasets used in the study area. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Evaluation of the improved random forest model. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Comparison of model metrics. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Flowchart of population spatialization. by Zhen Zhao (159931)

“…Subsequently, the feature factors corresponding to the model with the highest accuracy were selected as the optimal feature subsets and used in the model construction as input data. Additionally, considering the imbalanced in population spatial distribution, we used the K-means ++ clustering algorithm to cluster the optimal feature subset, and we used the bootstrap sampling method to extract the same amount of data from each cluster and fuse it with the training subset to build an improved random forest model. …”

Supplementary Material 9 by Nishitha R Kumar (19750617)

“…</li><li><b>Computational efficiency:</b> CD-HIT optimizes data processing by clustering sequences before applying machine learning models, making large-scale genomic analyses more feasible.…”